Download or read book Residual Attention Augmentation Graph Neural Network for Improved Node Classification Residual Attention Augmentation Graph Neural Network for Improved Node Classification written by Muhammad Affan Abbas and published by Infinite Study. This book was released on 2024-01-01 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Download or read book Data Representation and Attention aided Neural Networks for Object Classification and Segmentation written by Vinit Veerendraveer Singh and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of Convolutional Neural Networks can be primarily attributed to considering the underlying grid representation of images and the local neighborhoods around pixels. Another critical factor for their success is to implicitly and hierarchically attend to task-critical regions in the images. However, extending techniques and components from image-based Convolutional Neural Networks to higher dimensional data is non-trivial. In addition, deeper Convolutional Neural Networks are required to attend to the task-critical regions implicitly, thus increasing their computational complexity. The goal of this dissertation is to present novel techniques and components for neural networks that harness the data's underlying representation for computer vision tasks. It also presents attention modules that aid the neural network in explicitly attending to regions that are most likely to improve their performance on predictive modeling problems such as object classification and object segmentation. I first present an attention mechanism for feature maps of neural networks that takes advantage of the underlying grid representation of feature maps. When it was introduced to pre-trained Convolutional Networks' bottlenecks, consistent improvements in accuracy were observed for image classification. Second, I extended dilated convolutions from the image domain to 3D mesh representations. Then, I utilized dilated mesh convolutions to build novel attention and pooling mechanisms in neural networks for mesh classification. State-of-the-art results were achieved. Lastly, I present an attention-inspired and graph-representation-based data augmentation approach that generalizes to n-dimensional space. Utilizing this augmentation approach with neural networks improved their performance for image classification and 3D shape analysis tasks.

Download or read book Web and Big Data written by Leong Hou U and published by Springer Nature. This book was released on 2021-08-18 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 12858 and 12859, constitutes the thoroughly refereed proceedings of the 5th International Joint Conference, APWeb-WAIM 2021, held in Guangzhou, China, in August 2021. The 44 full papers presented together with 24 short papers, and 6 demonstration papers were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining; Data Management; Topic Model and Language Model Learning; Text Analysis; Text Classification; Machine Learning; Knowledge Graph; Emerging Data Processing Techniques; Information Extraction and Retrieval; Recommender System; Spatial and Spatio-Temporal Databases; and Demo.

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book Neural Information Processing written by Mohammad Tanveer and published by Springer Nature. This book was released on 2023-04-12 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing

Download or read book Outlier Detection for Temporal Data written by Manish Gupta and published by Springer. This book was released on 2014-04-14 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

Download or read book Graph Neural Networks for Improved Interpretability and Efficiency written by Patrick Pho and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation in their capability to make use of both network topology and node features. Graph Neural Networks (GNNs) provide an efficient framework combining both sources of information to produce accurate prediction for a wide range of tasks including node classification, link prediction, etc. The exponential growth of graph datasets drives the development of complex GNN models causing concerns about processing time and interpretability of the result. Another issue arises from the cost and limitation of collecting a large amount of annotated data for training deep learning GNN models. Apart from sampling issue, the existence of anomaly entities in the data might degrade the quality of the fitted models. In this dissertation, we propose novel techniques and strategies to overcome the above challenges. First, we present a flexible regularization scheme applied to the Simple Graph Convolution (SGC). The proposed framework inherits fast and efficient properties of SGC while rendering a sparse set of fitted parameter vectors, facilitating the identification of important input features. Next, we examine efficient procedures for collecting training samples and develop indicative measures as well as quantitative guidelines to assist practitioners in choosing the optimal sampling strategy to obtain data. We then improve upon an existing GNN model for the anomaly detection task. Our proposed framework achieves better accuracy and reliability. Lastly, we experiment with adapting the flexible regularization mechanism to link prediction task.

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Zhiyuan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Download or read book State of the Art in Neural Networks and Their Applications written by Jasjit Suri and published by Elsevier. This book was released on 2022-11-29 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer's disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks. - Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of oncology imaging technologies - Provides in-depth technical coverage of computer-aided diagnosis (CAD), including coverage of computer-aided classification, unified deep learning frameworks, 3D MRI, PET/CT, and more - Covers deep learning cancer identification from histopathological images, medical image analysis, detection, segmentation and classification via AI

Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Download or read book Advances in Computer Graphics written by Nadia Magnenat-Thalmann and published by Springer Nature. This book was released on 2020-10-17 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 37th Computer Graphics International Conference, CGI 2020, held in Geneva, Switzerland, in October 2020. The conference was held virtually. The 43 full papers presented together with 3 short papers were carefully reviewed and selected from 189 submissions. The papers address topics such as: virtual reality; rendering and textures; augmented and mixed reality; video processing; image processing; fluid simulation and control; meshes and topology; visual simulation and aesthetics; human computer interaction; computer animation; geometric computing; robotics and vision; scientific visualization; and machine learning for graphics.

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.

Download or read book Improved Deep Convolutional Neural Networks DCNN Approaches for Computer Vision and Bio medical Imaging written by Md Zahangir Alom and published by . This book was released on 2018 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is showing tremendous success in variety of application domains and demonstrates state-of-the-art performance over traditional machine learning approaches in the fields of Computer Vision, Speech Recognition, Natural Language Processing (NLP), Bio-Medical imaging, Computational Pathology, and many more. This thesis presents several improved Deep Convolutional Neural Network (DCNN) models including the Inception Recurrent Convolutional Neural Network (IRCNN) and Inception Recurrent Residual Convolutional Neural Networks (IRRCNN), a Recurrent U-Net (RU-Net), a Recurrent Residual U-Net (R2U-Net) model, a R2U-Net regression model, and a Densely Connected Recurrent Network (DCRN). These models are evaluated for classification, segmentation, and detection tasks in computer vision, Bio-medical imaging, and computational pathology applications. There are four key contribution areas in this thesis.The first contribution area is the introduction of two improved DCNN models for classification tasks: IRCNN and IRRCNN, which utilize the power of the Recurrent Convolutional Neural Network (RCNN), the Inception Network, and the Residual Network (ResNet). In addition, we have evaluated the impact of recurrent convolutional layers on DenseNet which is called Densely Connected Recurrent Network (DCRN). The performance of the IRCNN, DCRN, and IRRCNN models was investigated with a set of experiments and computer vision tasks where we used several publicly available datasets including MNIST, CIFAR 10, CIFAR 100, SVHN, CU3D-100, and Tiny ImageNet-200. The experimental results show that IRCNN, DCRN, and IRRCNN provide superior performance compared to the equivalent DCNN based methods including equivalent RCNN, ResNet, Inception V3, DenseNet, and Inception Residual Network (Inception V-4) with the same number of network parameters for different computer vision tasks. The second contribution area is the introduction of two different models including a Recurrent U-Net and Recurrent Residual U-Net models, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, the Residual Network, the RCNN, and U-Net for image segmentation tasks. These proposed architectures have several advantages for segmentation tasks over the existing DL methods. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets for blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including SegNet, U-Net and Residual U-Net (ResU-Net) in different Bio-medical segmentation tasks.The third contribution area is the introduction of an R2U-Net based regression model which is named University of Dayton Network (UD-Net) and is used for end-to-end detection tasks in digital pathology. To generalize these advanced DCNN models, we have applied classification, segmentation, and detection tasks in Digital Pathology Image Analysis (DPIA) including: microscopic blood cell classification, Breast Cancer Classification (BCC), invasive ductal carcinoma detection, and lymphoma classification, nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, and mitosis detection. The experiments have been conducted on different publicly available datasets and evaluated with different performance metrics. The results demonstrate superior performance compared to existing DCNN based methods. The fourth contribution area is the introduction of an image reconstruction technique using Convolutional Sparse Coding (CSC) on IBM's TrueNorth Neuromorphic computing system and the results demonstrate promising sparse reconstructions for two different benchmarks: MNIST and CIFAR-10. In 2016, IBM's release of a deep learning framework for DCNNs called Energy Efficient Deep Neuromorphic Networks (EEDN). EEDN shows promise for delivering high accuracies across different benchmark while consuming very low power using IBM's TrueNorth chip. We have empirically evaluated the performance of different DCNN architectures implemented within the EEDN framework to discover the most efficient way to implement DCNN models for object classification tasks using the TrueNorth system. The results show that for datasets with large numbers of classes, wider networks perform better when compared to deep networks comprised of nearly the same core complexity on IBM's TrueNorth system. In addition, we have proposed an effective quantization approach for Recurrent Neural Networks (RNN): Long Short-Term Memory (SLTM), Gated Recurrent Unit (GRU), and Convolutional LSTM (ConvLSTM). Furthermore, an NP-hard optimization problem called Quadratic Unconstrained Binary Optimization (QUBO) has solved with vanilla RNN on IBM's Neuromorphic computing system.